Order-constrained inference: a nuanced approach to hypothesis testing
Chen, Meichai
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https://hdl.handle.net/2142/124465
Description
Title
Order-constrained inference: a nuanced approach to hypothesis testing
Author(s)
Chen, Meichai
Issue Date
2024-05-03
Director of Research (if dissertation) or Advisor (if thesis)
Regenwetter, Michel
Committee Member(s)
Koehn, Hans Friedrich
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Order-constrained Inference
Nuanced Hypotheses
Model Competition
Language
eng
Abstract
Many statistical analyses performed in psychological studies add extraneous assumptions that are not part of the theory. These added assumptions could adversely influence the conclusions one derives from the analyses. Order-constrained inference allows researchers to avoid unnecessary assumptions, translate verbal predictions into direct testable hypotheses, and run model selection among competing theories. We reanalyzed data from two separate case studies to highlight how one can use order-constrained modeling to formulate more nuanced hypotheses and test these hypotheses jointly. To further leverage order-constrained inference, we compared the performance of competing theories using Bayesian model selection methods in the second case study. We observe that order-constrained inference not only provides us with a coarse view of all the hypotheses at the joint level, it also offers a fine-grained perspective of all the hypotheses at the item level that might otherwise stay hidden if we only assessed trends at the aggregate level.
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